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1.
  • Aydemir, Alper, et al. (author)
  • Kinect@Home : Crowdsourcing a large 3D dataset of real environments
  • 2012
  • In: AAAI Spring Symposium - Technical Report. - 9781577355557 ; , s. 8-9
  • Conference paper (peer-reviewed)abstract
    • We present Kinect@Home, aimed at collecting a vast RGB-D dataset from real everyday living spaces. This dataset is planned to be the largest real world image collection of everyday environments to date, making use of the availability of a widely adopted robotics sensor which is also in the homes of millions of users, the Microsoft Kinect camera.
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2.
  • Raabe, Chris, et al. (author)
  • Aggressive navigation using high-speed natural feature point tracking
  • 2014
  • In: IEEE Aerospace Conference Proceedings. - 9781479916221
  • Conference paper (peer-reviewed)abstract
    • Presently, most autonomous aerial vehicles rely on satellite navigation such as GPS to sense their position in the earth reference frame. However, reliance on GPS restricts the vehicle to missions where GPS signals are readily received. Motion capture systems are capable of indoor localization but require large infrastructure and are prone to occlusion. To overcome these restrictions, a self-contained high-speed vision system was developed at the University of Tokyo in collaboration with Boeing Research & Technology. The system has been flight tested and shown to be capable of drift-free position and attitude estimates without any reliance on GPS signals. Furthermore, the positional accuracy and update rate is at least one order of magnitude superior to that of uncorrected GPS.
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  • Result 1-2 of 2
Type of publication
conference paper (2)
Type of content
peer-reviewed (2)
Author/Editor
Henell, Daniel (2)
Jensfelt, Patric (1)
Aydemir, Alper (1)
Shilkrot, R. (1)
Raabe, Chris (1)
Saad, E. (1)
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Vian, J. (1)
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University
Royal Institute of Technology (2)
Language
English (2)
Research subject (UKÄ/SCB)
Natural sciences (1)
Engineering and Technology (1)

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